Overview

Dataset statistics

Number of variables27
Number of observations61249
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.6 MiB
Average record size in memory216.0 B

Variable types

Numeric24
Categorical3

Alerts

Cycle is highly overall correlated with RULHigh correlation
Operation Setting 1 is highly overall correlated with Operation Setting 2 and 20 other fieldsHigh correlation
Operation Setting 2 is highly overall correlated with Operation Setting 1 and 20 other fieldsHigh correlation
Operation Setting 3 is highly overall correlated with Operation Setting 1 and 14 other fieldsHigh correlation
RUL is highly overall correlated with CycleHigh correlation
Sensor Measure 1 is highly overall correlated with Operation Setting 1 and 19 other fieldsHigh correlation
Sensor Measure 10 is highly overall correlated with Operation Setting 1 and 22 other fieldsHigh correlation
Sensor Measure 11 is highly overall correlated with Operation Setting 1 and 21 other fieldsHigh correlation
Sensor Measure 12 is highly overall correlated with Operation Setting 1 and 20 other fieldsHigh correlation
Sensor Measure 13 is highly overall correlated with Operation Setting 3 and 5 other fieldsHigh correlation
Sensor Measure 14 is highly overall correlated with Operation Setting 3 and 17 other fieldsHigh correlation
Sensor Measure 15 is highly overall correlated with Operation Setting 1 and 21 other fieldsHigh correlation
Sensor Measure 16 is highly overall correlated with Operation Setting 1 and 19 other fieldsHigh correlation
Sensor Measure 17 is highly overall correlated with Operation Setting 1 and 21 other fieldsHigh correlation
Sensor Measure 18 is highly overall correlated with Operation Setting 1 and 21 other fieldsHigh correlation
Sensor Measure 19 is highly overall correlated with Operation Setting 1 and 16 other fieldsHigh correlation
Sensor Measure 2 is highly overall correlated with Operation Setting 1 and 21 other fieldsHigh correlation
Sensor Measure 20 is highly overall correlated with Operation Setting 1 and 19 other fieldsHigh correlation
Sensor Measure 21 is highly overall correlated with Operation Setting 1 and 19 other fieldsHigh correlation
Sensor Measure 3 is highly overall correlated with Operation Setting 1 and 21 other fieldsHigh correlation
Sensor Measure 4 is highly overall correlated with Operation Setting 1 and 21 other fieldsHigh correlation
Sensor Measure 5 is highly overall correlated with Operation Setting 1 and 18 other fieldsHigh correlation
Sensor Measure 6 is highly overall correlated with Operation Setting 1 and 18 other fieldsHigh correlation
Sensor Measure 7 is highly overall correlated with Operation Setting 1 and 19 other fieldsHigh correlation
Sensor Measure 8 is highly overall correlated with Operation Setting 1 and 22 other fieldsHigh correlation
Sensor Measure 9 is highly overall correlated with Operation Setting 1 and 22 other fieldsHigh correlation
Operation Setting 2 has 4776 (7.8%) zerosZeros

Reproduction

Analysis started2024-06-01 18:43:23.417302
Analysis finished2024-06-01 18:45:04.439136
Duration1 minute and 41.02 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

UnitNumber
Real number (ℝ)

Distinct249
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.32518
Minimum1
Maximum249
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.6 KiB
2024-06-01T20:45:04.582657image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q160
median126
Q3185
95-th percentile237
Maximum249
Range248
Interquartile range (IQR)125

Descriptive statistics

Standard deviation71.99535
Coefficient of variation (CV)0.57908904
Kurtosis-1.1964396
Mean124.32518
Median Absolute Deviation (MAD)62
Skewness-0.01062972
Sum7614793
Variance5183.3304
MonotonicityIncreasing
2024-06-01T20:45:04.768021image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
118 543
 
0.9%
133 489
 
0.8%
173 457
 
0.7%
49 446
 
0.7%
179 435
 
0.7%
225 418
 
0.7%
171 399
 
0.7%
158 399
 
0.7%
37 388
 
0.6%
88 388
 
0.6%
Other values (239) 56887
92.9%
ValueCountFrequency (%)
1 321
0.5%
2 299
0.5%
3 307
0.5%
4 274
0.4%
5 193
0.3%
6 331
0.5%
7 221
0.4%
8 230
0.4%
9 334
0.5%
10 354
0.6%
ValueCountFrequency (%)
249 255
0.4%
248 184
0.3%
247 211
0.3%
246 161
0.3%
245 205
0.3%
244 267
0.4%
243 327
0.5%
242 222
0.4%
241 332
0.5%
240 149
0.2%

Cycle
Real number (ℝ)

HIGH CORRELATION 

Distinct543
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean134.31142
Minimum1
Maximum543
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.6 KiB
2024-06-01T20:45:04.975094image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q162
median123
Q3191
95-th percentile299
Maximum543
Range542
Interquartile range (IQR)129

Descriptive statistics

Standard deviation89.783389
Coefficient of variation (CV)0.66847176
Kurtosis0.24691862
Mean134.31142
Median Absolute Deviation (MAD)64
Skewness0.7079729
Sum8226440
Variance8061.057
MonotonicityNot monotonic
2024-06-01T20:45:05.166149image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 249
 
0.4%
65 249
 
0.4%
96 249
 
0.4%
95 249
 
0.4%
94 249
 
0.4%
93 249
 
0.4%
92 249
 
0.4%
91 249
 
0.4%
90 249
 
0.4%
89 249
 
0.4%
Other values (533) 58759
95.9%
ValueCountFrequency (%)
1 249
0.4%
2 249
0.4%
3 249
0.4%
4 249
0.4%
5 249
0.4%
6 249
0.4%
7 249
0.4%
8 249
0.4%
9 249
0.4%
10 249
0.4%
ValueCountFrequency (%)
543 1
< 0.1%
542 1
< 0.1%
541 1
< 0.1%
540 1
< 0.1%
539 1
< 0.1%
538 1
< 0.1%
537 1
< 0.1%
536 1
< 0.1%
535 1
< 0.1%
534 1
< 0.1%

Operation Setting 1
Real number (ℝ)

HIGH CORRELATION 

Distinct536
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.999823
Minimum0
Maximum42.008
Zeros162
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size478.6 KiB
2024-06-01T20:45:05.365196image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.001
Q110.0046
median25.0014
Q341.9981
95-th percentile42.006
Maximum42.008
Range42.008
Interquartile range (IQR)31.9935

Descriptive statistics

Standard deviation14.780722
Coefficient of variation (CV)0.61586794
Kurtosis-1.2307502
Mean23.999823
Median Absolute Deviation (MAD)14.9969
Skewness-0.26043963
Sum1469965.2
Variance218.46973
MonotonicityNot monotonic
2024-06-01T20:45:05.512240image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0022 339
 
0.6%
0.0015 333
 
0.5%
0.0023 324
 
0.5%
0.0019 323
 
0.5%
0.0006 323
 
0.5%
0.0028 322
 
0.5%
0.0024 321
 
0.5%
0.0005 319
 
0.5%
0.0016 318
 
0.5%
0.0027 317
 
0.5%
Other values (526) 58010
94.7%
ValueCountFrequency (%)
0 162
0.3%
0.0001 307
0.5%
0.0002 305
0.5%
0.0003 295
0.5%
0.0004 313
0.5%
0.0005 319
0.5%
0.0006 323
0.5%
0.0007 306
0.5%
0.0008 282
0.5%
0.0009 315
0.5%
ValueCountFrequency (%)
42.008 77
0.1%
42.0079 166
0.3%
42.0078 163
0.3%
42.0077 159
0.3%
42.0076 137
0.2%
42.0075 138
0.2%
42.0074 150
0.2%
42.0073 141
0.2%
42.0072 150
0.2%
42.0071 169
0.3%

Operation Setting 2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct105
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57134689
Minimum0
Maximum0.842
Zeros4776
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size478.6 KiB
2024-06-01T20:45:05.678651image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.2507
median0.7
Q30.84
95-th percentile0.8415
Maximum0.842
Range0.842
Interquartile range (IQR)0.5893

Descriptive statistics

Standard deviation0.31070344
Coefficient of variation (CV)0.54380876
Kurtosis-0.8796748
Mean0.57134689
Median Absolute Deviation (MAD)0.14
Skewness-0.82941362
Sum34994.426
Variance0.09653663
MonotonicityNot monotonic
2024-06-01T20:45:05.889066image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.84 12511
20.4%
0 4776
 
7.8%
0.25 4686
 
7.7%
0.62 4683
 
7.6%
0.7 4634
 
7.6%
0.8413 655
 
1.1%
0.8403 653
 
1.1%
0.8402 643
 
1.0%
0.8404 637
 
1.0%
0.8408 634
 
1.0%
Other values (95) 26737
43.7%
ValueCountFrequency (%)
0 4776
7.8%
0.0001 227
 
0.4%
0.0002 228
 
0.4%
0.0003 246
 
0.4%
0.0004 227
 
0.4%
0.0005 227
 
0.4%
0.0006 244
 
0.4%
0.0007 234
 
0.4%
0.0008 215
 
0.4%
0.0009 207
 
0.3%
ValueCountFrequency (%)
0.842 344
0.6%
0.8419 584
1.0%
0.8418 594
1.0%
0.8417 582
1.0%
0.8416 631
1.0%
0.8415 619
1.0%
0.8414 618
1.0%
0.8413 655
1.1%
0.8412 589
1.0%
0.8411 604
1.0%

Operation Setting 3
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
100.0
52110 
60.0
9139 

Length

Max length5
Median length5
Mean length4.8507894
Min length4

Characters and Unicode

Total characters297106
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100.0
2nd row100.0
3rd row100.0
4th row100.0
5th row60.0

Common Values

ValueCountFrequency (%)
100.0 52110
85.1%
60.0 9139
 
14.9%

Length

2024-06-01T20:45:06.029513image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-01T20:45:06.133521image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
100.0 52110
85.1%
60.0 9139
 
14.9%

Most occurring characters

ValueCountFrequency (%)
0 174608
58.8%
. 61249
 
20.6%
1 52110
 
17.5%
6 9139
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 297106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 174608
58.8%
. 61249
 
20.6%
1 52110
 
17.5%
6 9139
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 297106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 174608
58.8%
. 61249
 
20.6%
1 52110
 
17.5%
6 9139
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 297106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 174608
58.8%
. 61249
 
20.6%
1 52110
 
17.5%
6 9139
 
3.1%

Sensor Measure 1
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean472.88244
Minimum445
Maximum518.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.6 KiB
2024-06-01T20:45:06.251069image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum445
5-th percentile445
Q1445
median462.54
Q3491.19
95-th percentile518.67
Maximum518.67
Range73.67
Interquartile range (IQR)46.19

Descriptive statistics

Standard deviation26.436832
Coefficient of variation (CV)0.055905717
Kurtosis-1.1805952
Mean472.88244
Median Absolute Deviation (MAD)17.54
Skewness0.45924728
Sum28963576
Variance698.90607
MonotonicityNot monotonic
2024-06-01T20:45:06.361748image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
445 15395
25.1%
518.67 9238
15.1%
489.05 9224
15.1%
449.44 9162
15.0%
462.54 9139
14.9%
491.19 9091
14.8%
ValueCountFrequency (%)
445 15395
25.1%
449.44 9162
15.0%
462.54 9139
14.9%
489.05 9224
15.1%
491.19 9091
14.8%
518.67 9238
15.1%
ValueCountFrequency (%)
518.67 9238
15.1%
491.19 9091
14.8%
489.05 9224
15.1%
462.54 9139
14.9%
449.44 9162
15.0%
445 15395
25.1%

Sensor Measure 2
Real number (ℝ)

HIGH CORRELATION 

Distinct1704
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean579.42006
Minimum535.48
Maximum644.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.6 KiB
2024-06-01T20:45:06.497559image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum535.48
5-th percentile536.57
Q1549.33
median555.74
Q3607.07
95-th percentile642.69
Maximum644.42
Range108.94
Interquartile range (IQR)57.74

Descriptive statistics

Standard deviation37.342647
Coefficient of variation (CV)0.064448316
Kurtosis-1.3097566
Mean579.42006
Median Absolute Deviation (MAD)19.17
Skewness0.44429312
Sum35488899
Variance1394.4733
MonotonicityNot monotonic
2024-06-01T20:45:06.678047image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
549.36 162
 
0.3%
549.25 160
 
0.3%
549.47 154
 
0.3%
549.55 148
 
0.2%
549.3 146
 
0.2%
549.48 146
 
0.2%
549.42 145
 
0.2%
549.43 143
 
0.2%
549.49 142
 
0.2%
549.39 142
 
0.2%
Other values (1694) 59761
97.6%
ValueCountFrequency (%)
535.48 2
< 0.1%
535.51 1
< 0.1%
535.56 1
< 0.1%
535.58 1
< 0.1%
535.59 1
< 0.1%
535.61 2
< 0.1%
535.62 1
< 0.1%
535.63 2
< 0.1%
535.64 1
< 0.1%
535.65 2
< 0.1%
ValueCountFrequency (%)
644.42 1
 
< 0.1%
644.21 1
 
< 0.1%
644.2 3
< 0.1%
644.19 1
 
< 0.1%
644.17 1
 
< 0.1%
644.16 1
 
< 0.1%
644.15 1
 
< 0.1%
644.14 1
 
< 0.1%
644.13 1
 
< 0.1%
644.12 2
< 0.1%

Sensor Measure 3
Real number (ℝ)

HIGH CORRELATION 

Distinct13558
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1417.8966
Minimum1242.67
Maximum1613
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.6 KiB
2024-06-01T20:45:06.827883image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1242.67
5-th percentile1257.96
Q11350.55
median1367.68
Q31497.42
95-th percentile1591.13
Maximum1613
Range370.33
Interquartile range (IQR)146.87

Descriptive statistics

Standard deviation106.1676
Coefficient of variation (CV)0.074876827
Kurtosis-1.1334546
Mean1417.8966
Median Absolute Deviation (MAD)107.97
Skewness0.16951483
Sum86844749
Variance11271.559
MonotonicityNot monotonic
2024-06-01T20:45:07.032260image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1355.66 22
 
< 0.1%
1356.86 21
 
< 0.1%
1355.78 21
 
< 0.1%
1354.67 18
 
< 0.1%
1354.47 18
 
< 0.1%
1353.86 18
 
< 0.1%
1350.48 18
 
< 0.1%
1352.09 18
 
< 0.1%
1358.46 18
 
< 0.1%
1360.71 18
 
< 0.1%
Other values (13548) 61059
99.7%
ValueCountFrequency (%)
1242.67 1
< 0.1%
1242.98 1
< 0.1%
1243.28 1
< 0.1%
1243.51 1
< 0.1%
1243.62 1
< 0.1%
1243.66 1
< 0.1%
1244 1
< 0.1%
1244.3 1
< 0.1%
1244.48 1
< 0.1%
1244.64 1
< 0.1%
ValueCountFrequency (%)
1613 2
< 0.1%
1612.66 1
< 0.1%
1611.9 1
< 0.1%
1611.42 1
< 0.1%
1609.79 1
< 0.1%
1609.76 2
< 0.1%
1609.7 1
< 0.1%
1609.03 1
< 0.1%
1608.88 1
< 0.1%
1608.46 2
< 0.1%

Sensor Measure 4
Real number (ℝ)

HIGH CORRELATION 

Distinct17353
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1201.9154
Minimum1024.42
Maximum1440.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.6 KiB
2024-06-01T20:45:07.255854image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1024.42
5-th percentile1043.014
Q11119.49
median1136.92
Q31302.62
95-th percentile1408.57
Maximum1440.77
Range416.35
Interquartile range (IQR)183.13

Descriptive statistics

Standard deviation119.32759
Coefficient of variation (CV)0.099281193
Kurtosis-1.1055536
Mean1201.9154
Median Absolute Deviation (MAD)93.81
Skewness0.43026883
Sum73616114
Variance14239.074
MonotonicityNot monotonic
2024-06-01T20:45:07.384755image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1123.98 22
 
< 0.1%
1128.74 20
 
< 0.1%
1125.1 20
 
< 0.1%
1123 19
 
< 0.1%
1122.33 19
 
< 0.1%
1119.71 18
 
< 0.1%
1123.92 18
 
< 0.1%
1126.15 18
 
< 0.1%
1124.99 18
 
< 0.1%
1132.52 18
 
< 0.1%
Other values (17343) 61059
99.7%
ValueCountFrequency (%)
1024.42 1
< 0.1%
1025.52 1
< 0.1%
1025.75 1
< 0.1%
1025.96 1
< 0.1%
1026.47 1
< 0.1%
1026.53 1
< 0.1%
1026.59 1
< 0.1%
1026.91 1
< 0.1%
1027.39 1
< 0.1%
1027.52 1
< 0.1%
ValueCountFrequency (%)
1440.77 1
 
< 0.1%
1437.97 1
 
< 0.1%
1437.77 1
 
< 0.1%
1436.78 1
 
< 0.1%
1436.1 1
 
< 0.1%
1435.88 3
< 0.1%
1435.39 1
 
< 0.1%
1435.28 1
 
< 0.1%
1435 1
 
< 0.1%
1434.87 1
 
< 0.1%

Sensor Measure 5
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0316258
Minimum3.91
Maximum14.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.6 KiB
2024-06-01T20:45:07.519592image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum3.91
5-th percentile3.91
Q13.91
median7.05
Q310.52
95-th percentile14.62
Maximum14.62
Range10.71
Interquartile range (IQR)6.61

Descriptive statistics

Standard deviation3.6228721
Coefficient of variation (CV)0.45107581
Kurtosis-0.85550491
Mean8.0316258
Median Absolute Deviation (MAD)3.14
Skewness0.53773997
Sum491929.05
Variance13.125202
MonotonicityNot monotonic
2024-06-01T20:45:07.655320image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3.91 15395
25.1%
14.62 9238
15.1%
10.52 9224
15.1%
5.48 9162
15.0%
7.05 9139
14.9%
9.35 9091
14.8%
ValueCountFrequency (%)
3.91 15395
25.1%
5.48 9162
15.0%
7.05 9139
14.9%
9.35 9091
14.8%
10.52 9224
15.1%
14.62 9238
15.1%
ValueCountFrequency (%)
14.62 9238
15.1%
10.52 9224
15.1%
9.35 9091
14.8%
7.05 9139
14.9%
5.48 9162
15.0%
3.91 15395
25.1%

Sensor Measure 6
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.589457
Minimum5.67
Maximum21.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.6 KiB
2024-06-01T20:45:07.783927image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum5.67
5-th percentile5.7
Q15.72
median9.03
Q315.48
95-th percentile21.61
Maximum21.61
Range15.94
Interquartile range (IQR)9.76

Descriptive statistics

Standard deviation5.4440165
Coefficient of variation (CV)0.4697387
Kurtosis-0.85523397
Mean11.589457
Median Absolute Deviation (MAD)3.33
Skewness0.62472165
Sum709842.66
Variance29.637316
MonotonicityNot monotonic
2024-06-01T20:45:07.943399image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
5.71 5737
 
9.4%
8 5192
 
8.5%
21.61 5104
 
8.3%
5.72 4673
 
7.6%
15.49 3783
 
6.2%
9.03 3692
 
6.0%
13.66 3195
 
5.2%
5.7 2467
 
4.0%
5.69 2319
 
3.8%
9.02 2259
 
3.7%
Other values (36) 22828
37.3%
ValueCountFrequency (%)
5.67 4
 
< 0.1%
5.68 195
 
0.3%
5.69 2319
3.8%
5.7 2467
4.0%
5.71 5737
9.4%
5.72 4673
7.6%
7.95 57
 
0.1%
7.96 371
 
0.6%
7.97 1300
 
2.1%
7.98 873
 
1.4%
ValueCountFrequency (%)
21.61 5104
8.3%
21.6 1602
 
2.6%
21.59 584
 
1.0%
21.58 930
 
1.5%
21.57 798
 
1.3%
21.56 172
 
0.3%
21.55 47
 
0.1%
21.54 1
 
< 0.1%
15.5 1599
 
2.6%
15.49 3783
6.2%

Sensor Measure 7
Real number (ℝ)

HIGH CORRELATION 

Distinct5926
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean283.32863
Minimum136.17
Maximum570.81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.6 KiB
2024-06-01T20:45:08.116735image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum136.17
5-th percentile138.21
Q1142.92
median194.96
Q3394.28
95-th percentile554.9
Maximum570.81
Range434.64
Interquartile range (IQR)251.36

Descriptive statistics

Standard deviation146.88021
Coefficient of variation (CV)0.51840934
Kurtosis-0.87893611
Mean283.32863
Median Absolute Deviation (MAD)56.75
Skewness0.69950886
Sum17353595
Variance21573.796
MonotonicityNot monotonic
2024-06-01T20:45:08.297424image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
138.53 117
 
0.2%
138.62 114
 
0.2%
138.61 112
 
0.2%
138.66 106
 
0.2%
138.51 105
 
0.2%
138.74 105
 
0.2%
138.64 105
 
0.2%
138.77 104
 
0.2%
138.47 104
 
0.2%
138.69 103
 
0.2%
Other values (5916) 60174
98.2%
ValueCountFrequency (%)
136.17 1
 
< 0.1%
136.31 1
 
< 0.1%
136.38 1
 
< 0.1%
136.43 1
 
< 0.1%
136.45 1
 
< 0.1%
136.5 1
 
< 0.1%
136.51 1
 
< 0.1%
136.54 1
 
< 0.1%
136.56 1
 
< 0.1%
136.57 3
< 0.1%
ValueCountFrequency (%)
570.81 1
< 0.1%
569.74 1
< 0.1%
569.69 1
< 0.1%
569.58 1
< 0.1%
569.34 1
< 0.1%
569.29 1
< 0.1%
569.16 1
< 0.1%
569.14 1
< 0.1%
569.07 1
< 0.1%
569.06 1
< 0.1%

Sensor Measure 8
Real number (ℝ)

HIGH CORRELATION 

Distinct1038
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2228.686
Minimum1914.72
Maximum2388.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.6 KiB
2024-06-01T20:45:08.438735image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1914.72
5-th percentile1915.32
Q12211.95
median2223.07
Q32323.93
95-th percentile2388.12
Maximum2388.64
Range473.92
Interquartile range (IQR)111.98

Descriptive statistics

Standard deviation145.34824
Coefficient of variation (CV)0.065217012
Kurtosis0.48832812
Mean2228.686
Median Absolute Deviation (MAD)95.9
Skewness-1.217583
Sum1.3650479 × 108
Variance21126.112
MonotonicityNot monotonic
2024-06-01T20:45:08.629699image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2211.95 477
 
0.8%
2211.94 474
 
0.8%
2211.97 454
 
0.7%
2211.96 446
 
0.7%
2211.98 433
 
0.7%
2211.99 430
 
0.7%
2388.08 421
 
0.7%
2211.93 420
 
0.7%
2212 416
 
0.7%
2212.01 410
 
0.7%
Other values (1028) 56868
92.8%
ValueCountFrequency (%)
1914.72 1
< 0.1%
1914.8 2
< 0.1%
1914.83 2
< 0.1%
1914.84 1
< 0.1%
1914.85 1
< 0.1%
1914.86 1
< 0.1%
1914.88 1
< 0.1%
1914.89 2
< 0.1%
1914.9 1
< 0.1%
1914.91 1
< 0.1%
ValueCountFrequency (%)
2388.64 1
 
< 0.1%
2388.6 2
 
< 0.1%
2388.59 2
 
< 0.1%
2388.58 1
 
< 0.1%
2388.57 1
 
< 0.1%
2388.56 2
 
< 0.1%
2388.55 9
< 0.1%
2388.54 8
< 0.1%
2388.53 13
< 0.1%
2388.52 8
< 0.1%

Sensor Measure 9
Real number (ℝ)

HIGH CORRELATION 

Distinct25297
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8524.6733
Minimum7984.51
Maximum9196.81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.6 KiB
2024-06-01T20:45:08.800848image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum7984.51
5-th percentile8007.724
Q18320.59
median8362.76
Q38777.25
95-th percentile9065.67
Maximum9196.81
Range1212.3
Interquartile range (IQR)456.66

Descriptive statistics

Standard deviation336.92755
Coefficient of variation (CV)0.039523807
Kurtosis-1.0972164
Mean8524.6733
Median Absolute Deviation (MAD)351.49
Skewness0.13335256
Sum5.2212772 × 108
Variance113520.17
MonotonicityNot monotonic
2024-06-01T20:45:08.963458image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8339.85 13
 
< 0.1%
8314.89 13
 
< 0.1%
8329.62 12
 
< 0.1%
8323.89 12
 
< 0.1%
8321.05 12
 
< 0.1%
8323.74 12
 
< 0.1%
8346.27 12
 
< 0.1%
8346.66 11
 
< 0.1%
8776.33 11
 
< 0.1%
8352.78 11
 
< 0.1%
Other values (25287) 61130
99.8%
ValueCountFrequency (%)
7984.51 1
< 0.1%
7984.59 1
< 0.1%
7985.18 1
< 0.1%
7986.72 1
< 0.1%
7987.18 1
< 0.1%
7987.93 1
< 0.1%
7988.07 1
< 0.1%
7988.35 1
< 0.1%
7989.04 1
< 0.1%
7989.05 1
< 0.1%
ValueCountFrequency (%)
9196.81 1
< 0.1%
9196.07 1
< 0.1%
9195.38 1
< 0.1%
9194.16 1
< 0.1%
9179.67 1
< 0.1%
9178.46 1
< 0.1%
9177.65 1
< 0.1%
9176.4 1
< 0.1%
9175.43 1
< 0.1%
9174.88 1
< 0.1%

Sensor Measure 10
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.096445
Minimum0.93
Maximum1.32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.6 KiB
2024-06-01T20:45:09.095113image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.93
5-th percentile0.94
Q11.02
median1.03
Q31.26
95-th percentile1.3
Maximum1.32
Range0.39
Interquartile range (IQR)0.24

Descriptive statistics

Standard deviation0.12768053
Coefficient of variation (CV)0.11644955
Kurtosis-1.2012099
Mean1.096445
Median Absolute Deviation (MAD)0.05
Skewness0.58846393
Sum67156.16
Variance0.016302317
MonotonicityNot monotonic
2024-06-01T20:45:09.232587image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1.02 18078
29.5%
0.94 8831
14.4%
1.26 8290
13.5%
1.3 8135
13.3%
1.08 5978
 
9.8%
1.03 4008
 
6.5%
1.07 2482
 
4.1%
1.04 1227
 
2.0%
1.31 1038
 
1.7%
1.01 794
 
1.3%
Other values (11) 2388
 
3.9%
ValueCountFrequency (%)
0.93 284
 
0.5%
0.94 8831
14.4%
0.95 24
 
< 0.1%
1.01 794
 
1.3%
1.02 18078
29.5%
1.03 4008
 
6.5%
1.04 1227
 
2.0%
1.05 448
 
0.7%
1.06 2
 
< 0.1%
1.07 2482
 
4.1%
ValueCountFrequency (%)
1.32 58
 
0.1%
1.31 1038
 
1.7%
1.3 8135
13.3%
1.29 7
 
< 0.1%
1.28 5
 
< 0.1%
1.27 769
 
1.3%
1.26 8290
13.5%
1.25 160
 
0.3%
1.1 69
 
0.1%
1.09 562
 
0.9%

Sensor Measure 11
Real number (ℝ)

HIGH CORRELATION 

Distinct737
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.874529
Minimum36.04
Maximum48.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.6 KiB
2024-06-01T20:45:09.382637image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum36.04
5-th percentile36.56
Q141.76
median42.33
Q345.22
95-th percentile47.54
Maximum48.36
Range12.32
Interquartile range (IQR)3.46

Descriptive statistics

Standard deviation3.2434915
Coefficient of variation (CV)0.075650779
Kurtosis-0.33017191
Mean42.874529
Median Absolute Deviation (MAD)2.11
Skewness-0.53816542
Sum2626022
Variance10.520237
MonotonicityNot monotonic
2024-06-01T20:45:09.538972image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.8 353
 
0.6%
41.92 351
 
0.6%
41.84 349
 
0.6%
41.98 346
 
0.6%
41.93 344
 
0.6%
41.85 338
 
0.6%
41.89 335
 
0.5%
41.95 335
 
0.5%
41.94 334
 
0.5%
41.9 332
 
0.5%
Other values (727) 57832
94.4%
ValueCountFrequency (%)
36.04 1
 
< 0.1%
36.07 1
 
< 0.1%
36.08 1
 
< 0.1%
36.1 2
 
< 0.1%
36.11 2
 
< 0.1%
36.14 3
 
< 0.1%
36.15 6
< 0.1%
36.16 5
< 0.1%
36.17 7
< 0.1%
36.18 9
< 0.1%
ValueCountFrequency (%)
48.36 1
 
< 0.1%
48.34 2
 
< 0.1%
48.33 2
 
< 0.1%
48.32 4
< 0.1%
48.31 7
< 0.1%
48.3 3
< 0.1%
48.29 2
 
< 0.1%
48.28 5
< 0.1%
48.27 3
< 0.1%
48.26 4
< 0.1%

Sensor Measure 12
Real number (ℝ)

HIGH CORRELATION 

Distinct5627
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean266.73567
Minimum128.31
Maximum537.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.6 KiB
2024-06-01T20:45:09.696359image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum128.31
5-th percentile130.2
Q1134.52
median183.45
Q3371.4
95-th percentile522.71
Maximum537.49
Range409.18
Interquartile range (IQR)236.88

Descriptive statistics

Standard deviation138.47911
Coefficient of variation (CV)0.51916233
Kurtosis-0.87820552
Mean266.73567
Median Absolute Deviation (MAD)53.25
Skewness0.70070013
Sum16337293
Variance19176.464
MonotonicityNot monotonic
2024-06-01T20:45:09.834892image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130.56 136
 
0.2%
130.54 131
 
0.2%
130.46 128
 
0.2%
130.65 128
 
0.2%
130.67 126
 
0.2%
130.57 125
 
0.2%
130.52 125
 
0.2%
130.58 124
 
0.2%
130.85 124
 
0.2%
130.61 124
 
0.2%
Other values (5617) 59978
97.9%
ValueCountFrequency (%)
128.31 1
 
< 0.1%
128.45 1
 
< 0.1%
128.46 1
 
< 0.1%
128.54 2
< 0.1%
128.56 1
 
< 0.1%
128.57 1
 
< 0.1%
128.58 1
 
< 0.1%
128.59 2
< 0.1%
128.62 3
< 0.1%
128.71 1
 
< 0.1%
ValueCountFrequency (%)
537.49 1
< 0.1%
537.3 1
< 0.1%
537.06 1
< 0.1%
536.81 1
< 0.1%
536.8 1
< 0.1%
536.78 1
< 0.1%
536.66 1
< 0.1%
536.58 1
< 0.1%
536.56 1
< 0.1%
536.46 1
< 0.1%

Sensor Measure 13
Real number (ℝ)

HIGH CORRELATION 

Distinct483
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2334.4276
Minimum2027.57
Maximum2390.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.6 KiB
2024-06-01T20:45:10.020118image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2027.57
5-th percentile2028.21
Q12387.91
median2388.06
Q32388.17
95-th percentile2388.49
Maximum2390.49
Range362.92
Interquartile range (IQR)0.26

Descriptive statistics

Standard deviation128.19786
Coefficient of variation (CV)0.054916186
Kurtosis1.8775549
Mean2334.4276
Median Absolute Deviation (MAD)0.13
Skewness-1.969129
Sum1.4298136 × 108
Variance16434.691
MonotonicityNot monotonic
2024-06-01T20:45:10.167276image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2388.09 1610
 
2.6%
2388.07 1586
 
2.6%
2388.06 1583
 
2.6%
2388.1 1540
 
2.5%
2388.05 1495
 
2.4%
2388.08 1477
 
2.4%
2388.12 1452
 
2.4%
2388.04 1452
 
2.4%
2388.11 1449
 
2.4%
2388.03 1381
 
2.3%
Other values (473) 46224
75.5%
ValueCountFrequency (%)
2027.57 1
 
< 0.1%
2027.6 1
 
< 0.1%
2027.61 1
 
< 0.1%
2027.69 2
< 0.1%
2027.7 2
< 0.1%
2027.74 1
 
< 0.1%
2027.75 1
 
< 0.1%
2027.76 2
< 0.1%
2027.77 3
< 0.1%
2027.79 2
< 0.1%
ValueCountFrequency (%)
2390.49 1
 
< 0.1%
2390.28 1
 
< 0.1%
2390.17 1
 
< 0.1%
2390.16 4
< 0.1%
2390.11 2
< 0.1%
2390.06 2
< 0.1%
2390.05 1
 
< 0.1%
2390.04 2
< 0.1%
2390.03 1
 
< 0.1%
2389.99 2
< 0.1%

Sensor Measure 14
Real number (ℝ)

HIGH CORRELATION 

Distinct15938
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8067.8118
Minimum7845.78
Maximum8261.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.6 KiB
2024-06-01T20:45:10.347442image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum7845.78
5-th percentile7872.27
Q18062.63
median8083.81
Q38128.35
95-th percentile8153.09
Maximum8261.65
Range415.87
Interquartile range (IQR)65.72

Descriptive statistics

Standard deviation85.670543
Coefficient of variation (CV)0.010618808
Kurtosis0.90443623
Mean8067.8118
Median Absolute Deviation (MAD)30.67
Skewness-1.4095866
Sum4.9414541 × 108
Variance7339.442
MonotonicityNot monotonic
2024-06-01T20:45:10.481920image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8134.61 18
 
< 0.1%
8082.59 17
 
< 0.1%
8084.54 17
 
< 0.1%
8077.66 17
 
< 0.1%
8075.41 17
 
< 0.1%
8067.12 17
 
< 0.1%
8072.72 16
 
< 0.1%
8063.26 16
 
< 0.1%
8090.7 16
 
< 0.1%
8075.05 16
 
< 0.1%
Other values (15928) 61082
99.7%
ValueCountFrequency (%)
7845.78 1
< 0.1%
7849.79 1
< 0.1%
7851.03 1
< 0.1%
7851.3 1
< 0.1%
7852.67 1
< 0.1%
7853.09 1
< 0.1%
7853.14 1
< 0.1%
7854.01 1
< 0.1%
7854.7 1
< 0.1%
7854.81 1
< 0.1%
ValueCountFrequency (%)
8261.65 1
< 0.1%
8261.46 1
< 0.1%
8253.57 1
< 0.1%
8251.61 1
< 0.1%
8251.45 1
< 0.1%
8247.28 1
< 0.1%
8244.91 1
< 0.1%
8243.76 1
< 0.1%
8243.17 1
< 0.1%
8241.79 1
< 0.1%

Sensor Measure 15
Real number (ℝ)

HIGH CORRELATION 

Distinct11915
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.2856036
Minimum8.1757
Maximum11.0663
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.6 KiB
2024-06-01T20:45:10.611567image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum8.1757
5-th percentile8.37844
Q18.648
median9.2556
Q39.3658
95-th percentile10.8966
Maximum11.0663
Range2.8906
Interquartile range (IQR)0.7178

Descriptive statistics

Standard deviation0.75037369
Coefficient of variation (CV)0.080810437
Kurtosis0.36037415
Mean9.2856036
Median Absolute Deviation (MAD)0.1509
Skewness1.0400056
Sum568733.93
Variance0.56306067
MonotonicityNot monotonic
2024-06-01T20:45:10.748665image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.3182 29
 
< 0.1%
9.3074 27
 
< 0.1%
9.371 26
 
< 0.1%
9.3363 26
 
< 0.1%
9.3437 25
 
< 0.1%
9.3529 25
 
< 0.1%
9.354 25
 
< 0.1%
9.3012 25
 
< 0.1%
9.3479 25
 
< 0.1%
9.3491 24
 
< 0.1%
Other values (11905) 60992
99.6%
ValueCountFrequency (%)
8.1757 1
< 0.1%
8.1787 1
< 0.1%
8.1793 1
< 0.1%
8.185 1
< 0.1%
8.1976 1
< 0.1%
8.2011 1
< 0.1%
8.2024 2
< 0.1%
8.2052 1
< 0.1%
8.208 1
< 0.1%
8.2085 1
< 0.1%
ValueCountFrequency (%)
11.0663 1
< 0.1%
11.0572 1
< 0.1%
11.0534 1
< 0.1%
11.0506 1
< 0.1%
11.0505 1
< 0.1%
11.0503 1
< 0.1%
11.0467 1
< 0.1%
11.0466 1
< 0.1%
11.0436 1
< 0.1%
11.0431 1
< 0.1%

Sensor Measure 16
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
0.02
41328 
0.03
19921 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters244996
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.02
2nd row0.02
3rd row0.02
4th row0.02
5th row0.02

Common Values

ValueCountFrequency (%)
0.02 41328
67.5%
0.03 19921
32.5%

Length

2024-06-01T20:45:10.886512image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-01T20:45:10.984957image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.02 41328
67.5%
0.03 19921
32.5%

Most occurring characters

ValueCountFrequency (%)
0 122498
50.0%
. 61249
25.0%
2 41328
 
16.9%
3 19921
 
8.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 244996
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 122498
50.0%
. 61249
25.0%
2 41328
 
16.9%
3 19921
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 244996
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 122498
50.0%
. 61249
25.0%
2 41328
 
16.9%
3 19921
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 244996
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 122498
50.0%
. 61249
25.0%
2 41328
 
16.9%
3 19921
 
8.1%

Sensor Measure 17
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean347.76003
Minimum302
Maximum399
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.6 KiB
2024-06-01T20:45:11.106470image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum302
5-th percentile306
Q1330
median334
Q3368
95-th percentile393
Maximum399
Range97
Interquartile range (IQR)38

Descriptive statistics

Standard deviation27.808283
Coefficient of variation (CV)0.079964001
Kurtosis-1.1319222
Mean347.76003
Median Absolute Deviation (MAD)28
Skewness0.18475362
Sum21299954
Variance773.30063
MonotonicityNot monotonic
2024-06-01T20:45:11.254987image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
331 3904
 
6.4%
330 3858
 
6.3%
332 3610
 
5.9%
333 3401
 
5.6%
329 2683
 
4.4%
334 2667
 
4.4%
368 2413
 
3.9%
307 2404
 
3.9%
369 2382
 
3.9%
306 2292
 
3.7%
Other values (44) 31635
51.6%
ValueCountFrequency (%)
302 3
 
< 0.1%
303 36
 
0.1%
304 328
 
0.5%
305 1250
2.0%
306 2292
3.7%
307 2404
3.9%
308 1691
2.8%
309 808
 
1.3%
310 280
 
0.5%
311 47
 
0.1%
ValueCountFrequency (%)
399 1
 
< 0.1%
398 24
 
< 0.1%
397 145
 
0.2%
396 423
 
0.7%
395 810
 
1.3%
394 1315
2.1%
393 1968
3.2%
392 2193
3.6%
391 1538
2.5%
390 648
 
1.1%

Sensor Measure 18
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2228.6133
Minimum1915
Maximum2388
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.6 KiB
2024-06-01T20:45:11.375513image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1915
5-th percentile1915
Q12212
median2223
Q32324
95-th percentile2388
Maximum2388
Range473
Interquartile range (IQR)112

Descriptive statistics

Standard deviation145.47249
Coefficient of variation (CV)0.065274892
Kurtosis0.49048221
Mean2228.6133
Median Absolute Deviation (MAD)96
Skewness-1.2190946
Sum1.3650034 × 108
Variance21162.246
MonotonicityNot monotonic
2024-06-01T20:45:11.486151image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2212 15395
25.1%
2388 9238
15.1%
2319 9224
15.1%
2223 9162
15.0%
1915 9139
14.9%
2324 9091
14.8%
ValueCountFrequency (%)
1915 9139
14.9%
2212 15395
25.1%
2223 9162
15.0%
2319 9224
15.1%
2324 9091
14.8%
2388 9238
15.1%
ValueCountFrequency (%)
2388 9238
15.1%
2324 9091
14.8%
2319 9224
15.1%
2223 9162
15.0%
2212 15395
25.1%
1915 9139
14.9%

Sensor Measure 19
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
100.0
52110 
84.93
9139 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters306245
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100.0
2nd row100.0
3rd row100.0
4th row100.0
5th row84.93

Common Values

ValueCountFrequency (%)
100.0 52110
85.1%
84.93 9139
 
14.9%

Length

2024-06-01T20:45:11.610846image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-01T20:45:11.707476image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
100.0 52110
85.1%
84.93 9139
 
14.9%

Most occurring characters

ValueCountFrequency (%)
0 156330
51.0%
. 61249
 
20.0%
1 52110
 
17.0%
8 9139
 
3.0%
4 9139
 
3.0%
9 9139
 
3.0%
3 9139
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 306245
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 156330
51.0%
. 61249
 
20.0%
1 52110
 
17.0%
8 9139
 
3.0%
4 9139
 
3.0%
9 9139
 
3.0%
3 9139
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 306245
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 156330
51.0%
. 61249
 
20.0%
1 52110
 
17.0%
8 9139
 
3.0%
4 9139
 
3.0%
9 9139
 
3.0%
3 9139
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 306245
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 156330
51.0%
. 61249
 
20.0%
1 52110
 
17.0%
8 9139
 
3.0%
4 9139
 
3.0%
9 9139
 
3.0%
3 9139
 
3.0%

Sensor Measure 20
Real number (ℝ)

HIGH CORRELATION 

Distinct652
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.864333
Minimum10.16
Maximum39.89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.6 KiB
2024-06-01T20:45:11.861442image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum10.16
5-th percentile10.52
Q110.94
median14.93
Q328.56
95-th percentile39.08
Maximum39.89
Range29.73
Interquartile range (IQR)17.62

Descriptive statistics

Standard deviation9.9363961
Coefficient of variation (CV)0.47623837
Kurtosis-0.94146527
Mean20.864333
Median Absolute Deviation (MAD)4.41
Skewness0.64292583
Sum1277919.6
Variance98.731968
MonotonicityNot monotonic
2024-06-01T20:45:12.009741image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.6 534
 
0.9%
10.59 533
 
0.9%
10.61 527
 
0.9%
10.63 525
 
0.9%
10.65 519
 
0.8%
10.58 517
 
0.8%
10.64 513
 
0.8%
10.62 513
 
0.8%
10.57 509
 
0.8%
10.56 494
 
0.8%
Other values (642) 56065
91.5%
ValueCountFrequency (%)
10.16 1
 
< 0.1%
10.18 1
 
< 0.1%
10.19 1
 
< 0.1%
10.21 1
 
< 0.1%
10.22 2
< 0.1%
10.23 3
< 0.1%
10.24 1
 
< 0.1%
10.25 1
 
< 0.1%
10.26 3
< 0.1%
10.27 4
< 0.1%
ValueCountFrequency (%)
39.89 2
< 0.1%
39.84 1
 
< 0.1%
39.82 2
< 0.1%
39.79 1
 
< 0.1%
39.78 3
< 0.1%
39.76 3
< 0.1%
39.75 3
< 0.1%
39.74 3
< 0.1%
39.73 3
< 0.1%
39.72 4
< 0.1%

Sensor Measure 21
Real number (ℝ)

HIGH CORRELATION 

Distinct21574
Distinct (%)35.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.518995
Minimum6.0843
Maximum23.8852
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.6 KiB
2024-06-01T20:45:12.209899image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum6.0843
5-th percentile6.3132
Q16.5661
median8.9601
Q317.1355
95-th percentile23.448
Maximum23.8852
Range17.8009
Interquartile range (IQR)10.5694

Descriptive statistics

Standard deviation5.9626973
Coefficient of variation (CV)0.47629199
Kurtosis-0.9409452
Mean12.518995
Median Absolute Deviation (MAD)2.6468
Skewness0.64305508
Sum766775.96
Variance35.553759
MonotonicityNot monotonic
2024-06-01T20:45:12.360309image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.3688 18
 
< 0.1%
6.3664 18
 
< 0.1%
6.4243 16
 
< 0.1%
6.3897 16
 
< 0.1%
8.9282 16
 
< 0.1%
6.3608 16
 
< 0.1%
6.4149 16
 
< 0.1%
6.328 16
 
< 0.1%
6.3959 16
 
< 0.1%
6.3685 15
 
< 0.1%
Other values (21564) 61086
99.7%
ValueCountFrequency (%)
6.0843 1
< 0.1%
6.1008 1
< 0.1%
6.1185 1
< 0.1%
6.129 1
< 0.1%
6.1408 1
< 0.1%
6.1427 1
< 0.1%
6.1431 1
< 0.1%
6.1456 1
< 0.1%
6.1468 1
< 0.1%
6.149 1
< 0.1%
ValueCountFrequency (%)
23.8852 1
< 0.1%
23.8828 1
< 0.1%
23.8807 1
< 0.1%
23.8804 1
< 0.1%
23.8748 1
< 0.1%
23.8691 1
< 0.1%
23.867 1
< 0.1%
23.8669 1
< 0.1%
23.8649 1
< 0.1%
23.8639 1
< 0.1%

RUL
Real number (ℝ)

HIGH CORRELATION 

Distinct543
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean134.31142
Minimum1
Maximum543
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size478.6 KiB
2024-06-01T20:45:12.510488image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q162
median123
Q3191
95-th percentile299
Maximum543
Range542
Interquartile range (IQR)129

Descriptive statistics

Standard deviation89.783389
Coefficient of variation (CV)0.66847176
Kurtosis0.24691862
Mean134.31142
Median Absolute Deviation (MAD)64
Skewness0.7079729
Sum8226440
Variance8061.057
MonotonicityNot monotonic
2024-06-01T20:45:12.678949image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 249
 
0.4%
69 249
 
0.4%
97 249
 
0.4%
96 249
 
0.4%
95 249
 
0.4%
94 249
 
0.4%
93 249
 
0.4%
92 249
 
0.4%
91 249
 
0.4%
90 249
 
0.4%
Other values (533) 58759
95.9%
ValueCountFrequency (%)
1 249
0.4%
2 249
0.4%
3 249
0.4%
4 249
0.4%
5 249
0.4%
6 249
0.4%
7 249
0.4%
8 249
0.4%
9 249
0.4%
10 249
0.4%
ValueCountFrequency (%)
543 1
< 0.1%
542 1
< 0.1%
541 1
< 0.1%
540 1
< 0.1%
539 1
< 0.1%
538 1
< 0.1%
537 1
< 0.1%
536 1
< 0.1%
535 1
< 0.1%
534 1
< 0.1%

Interactions

2024-06-01T20:44:55.842596image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:29.932364image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:33.135543image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:36.364308image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:39.719716image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:43.098339image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:46.626908image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:49.920846image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:53.317877image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:56.375374image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:00.058812image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:03.544922image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:06.932625image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:10.123795image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:13.738071image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:17.062272image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:20.753271image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:24.251098image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:30.956591image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:34.276713image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:37.753942image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:41.123033image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:44.493274image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:47.859137image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:56.119331image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:30.160984image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:33.277055image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:36.479181image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:39.843904image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:43.260154image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:46.756357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:50.058109image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:53.439651image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:56.628015image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:00.179554image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:03.682109image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:07.060395image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:10.274698image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:13.868503image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:17.198714image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:20.942438image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:24.459264image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:31.111195image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:34.405277image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:37.891562image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:41.261406image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:44.614653image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:48.020538image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:56.385442image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:30.265217image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:33.432797image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:36.608768image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:39.964322image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:43.422980image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:46.923514image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:50.200273image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:53.568217image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:56.767810image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:00.298727image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:03.801083image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:07.187754image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:10.439548image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:14.030196image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:17.365513image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:21.110124image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:24.602037image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
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2024-06-01T20:43:55.765852image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:59.365660image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:02.801582image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:06.139930image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:09.468905image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:13.027513image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:16.389325image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:20.009937image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:23.594107image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:30.312280image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:33.652347image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:37.027402image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:40.444389image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:43.793092image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:47.198146image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:54.262066image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:45:01.728571image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:32.603694image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:35.827460image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:39.253972image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:42.564312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:46.051525image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:49.410044image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:52.790062image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:55.893052image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:59.540610image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:02.950972image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:06.255130image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:09.587089image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:13.156684image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:16.527791image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:20.213631image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:23.718944image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:30.444852image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:33.776587image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:37.150074image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:40.580940image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:43.933751image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:47.319738image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:54.568131image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:45:02.056138image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:32.743638image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:35.980099image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:39.360626image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:42.675485image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:46.178807image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:49.540081image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:52.913746image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:56.001622image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:59.661573image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:03.079897image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:06.393148image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:09.712935image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:13.281732image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:16.656268image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:20.352319image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:23.861643image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:30.549288image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:33.919642image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:37.281087image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:40.727823image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:44.058632image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:47.455853image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:54.880752image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:45:02.647786image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:32.880581image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:36.103563image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:39.512632image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:42.809584image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:46.321943image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:49.660701image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:53.036167image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:56.132109image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:59.782474image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:03.233616image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:06.655965image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:09.850117image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:13.417345image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:16.787177image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:20.472771image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:23.985930image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:30.660199image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:34.040081image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:37.427473image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:40.858539image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:44.241806image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:47.586396image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:55.242853image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:45:02.792389image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:33.022577image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:36.239177image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:39.608827image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:42.955184image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:46.502572image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:49.788357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:53.187694image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:56.254483image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:43:59.902556image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:03.394722image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:06.812748image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:09.997831image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:13.565149image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:16.934994image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:20.625085image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:24.138008image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:30.793228image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:34.158954image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:37.593483image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:40.995472image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:44.363143image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:47.718724image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:44:55.544032image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-06-01T20:45:12.817819image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
CycleOperation Setting 1Operation Setting 2Operation Setting 3RULSensor Measure 1Sensor Measure 10Sensor Measure 11Sensor Measure 12Sensor Measure 13Sensor Measure 14Sensor Measure 15Sensor Measure 16Sensor Measure 17Sensor Measure 18Sensor Measure 19Sensor Measure 2Sensor Measure 20Sensor Measure 21Sensor Measure 3Sensor Measure 4Sensor Measure 5Sensor Measure 6Sensor Measure 7Sensor Measure 8Sensor Measure 9UnitNumber
Cycle1.0000.0010.0010.000-0.671-0.0000.0670.1030.0410.3780.188-0.0630.0460.083-0.0010.0000.0470.0190.0190.0830.097-0.001-0.0260.0400.0880.114-0.009
Operation Setting 10.0011.0000.8871.000-0.001-0.943-0.741-0.710-0.927-0.082-0.4120.7130.953-0.761-0.7421.000-0.730-0.927-0.927-0.760-0.736-0.984-0.969-0.927-0.730-0.7590.001
Operation Setting 20.0010.8871.0001.000-0.000-0.820-0.592-0.581-0.7720.066-0.3910.5610.953-0.579-0.5091.000-0.501-0.773-0.773-0.578-0.582-0.902-0.888-0.772-0.501-0.5790.001
Operation Setting 30.0001.0001.0001.0000.0010.0360.6280.6170.2520.6170.617-0.6170.2910.6180.6271.0000.6170.2520.2520.6170.6170.0360.0360.2520.6170.6170.003
RUL-0.671-0.001-0.0000.0011.000-0.000-0.057-0.177-0.022-0.401-0.168-0.0180.081-0.1340.0000.008-0.0960.0110.012-0.133-0.1700.001-0.036-0.021-0.087-0.117-0.009
Sensor Measure 1-0.000-0.943-0.8200.036-0.0001.0000.7120.6820.9020.0770.280-0.6740.9530.7360.7961.0000.7830.9020.9020.7350.7070.9590.9440.9020.7830.7330.000
Sensor Measure 100.067-0.741-0.5920.628-0.0570.7121.0000.9540.8420.5060.790-0.9660.9590.9540.9161.0000.9040.8330.8330.9530.9550.7530.7380.8420.9190.9670.005
Sensor Measure 110.103-0.710-0.5810.617-0.1770.6820.9541.0000.7810.4940.787-0.8820.9740.9530.8831.0000.8990.7690.7690.9510.9790.7220.7330.7800.8840.9410.000
Sensor Measure 120.041-0.927-0.7720.252-0.0220.9020.8420.7811.0000.2730.527-0.8440.9530.8680.8510.5120.8360.9840.9840.8660.8230.9430.9260.9920.8510.8800.002
Sensor Measure 130.378-0.0820.0660.617-0.4010.0770.5060.4940.2731.0000.606-0.4950.2910.4960.4251.0000.4590.2400.2380.4960.4960.0840.0990.2720.5450.5530.018
Sensor Measure 140.188-0.412-0.3910.617-0.1680.2800.7900.7870.5270.6061.000-0.7820.8140.7350.5711.0000.5760.5110.5100.7340.7640.4190.4160.5260.6110.7780.014
Sensor Measure 15-0.0630.7130.561-0.617-0.018-0.674-0.966-0.882-0.844-0.495-0.7821.0000.955-0.910-0.8851.000-0.853-0.838-0.838-0.909-0.893-0.726-0.687-0.844-0.891-0.942-0.006
Sensor Measure 160.0460.9530.9530.2910.0810.9530.9590.9740.9530.2910.8140.9551.0000.8090.6730.2910.6700.7980.7980.8070.8100.8140.8060.7990.6700.805-0.001
Sensor Measure 170.083-0.761-0.5790.618-0.1340.7360.9540.9530.8680.4960.735-0.9100.8091.0000.9371.0000.9440.8600.8590.9820.9720.7740.7770.8680.9360.9750.001
Sensor Measure 18-0.001-0.742-0.5090.6270.0000.7960.9160.8830.8510.4250.571-0.8850.6730.9371.0001.0000.9840.8510.8510.9360.9080.7540.7430.8510.9840.9340.002
Sensor Measure 190.0001.0001.0001.0000.0081.0001.0001.0000.5121.0001.0001.0000.2911.0001.0001.0000.6170.2520.2520.6170.6170.0360.0360.2520.6170.6170.003
Sensor Measure 20.047-0.730-0.5010.617-0.0960.7830.9040.8990.8360.4590.576-0.8530.6700.9440.9840.6171.0000.8310.8310.9420.9230.7420.7460.8360.9750.9320.001
Sensor Measure 200.019-0.927-0.7730.2520.0110.9020.8330.7690.9840.2400.511-0.8380.7980.8600.8510.2520.8311.0000.9810.8580.8120.9430.9200.9840.8430.8680.002
Sensor Measure 210.019-0.927-0.7730.2520.0120.9020.8330.7690.9840.2380.510-0.8380.7980.8590.8510.2520.8310.9811.0000.8580.8120.9430.9200.9840.8430.8680.003
Sensor Measure 30.083-0.760-0.5780.617-0.1330.7350.9530.9510.8660.4960.734-0.9090.8070.9820.9360.6170.9420.8580.8581.0000.9710.7720.7760.8660.9350.9740.001
Sensor Measure 40.097-0.736-0.5820.617-0.1700.7070.9550.9790.8230.4960.764-0.8930.8100.9720.9080.6170.9230.8120.8120.9711.0000.7480.7570.8220.9080.960-0.000
Sensor Measure 5-0.001-0.984-0.9020.0360.0010.9590.7530.7220.9430.0840.419-0.7260.8140.7740.7540.0360.7420.9430.9430.7720.7481.0000.9850.9430.7420.772-0.001
Sensor Measure 6-0.026-0.969-0.8880.036-0.0360.9440.7380.7330.9260.0990.416-0.6870.8060.7770.7430.0360.7460.9200.9200.7760.7570.9851.0000.9250.7330.7660.003
Sensor Measure 70.040-0.927-0.7720.252-0.0210.9020.8420.7800.9920.2720.526-0.8440.7990.8680.8510.2520.8360.9840.9840.8660.8220.9430.9251.0000.8510.8790.003
Sensor Measure 80.088-0.730-0.5010.617-0.0870.7830.9190.8840.8510.5450.611-0.8910.6700.9360.9840.6170.9750.8430.8430.9350.9080.7420.7330.8511.0000.9500.006
Sensor Measure 90.114-0.759-0.5790.617-0.1170.7330.9670.9410.8800.5530.778-0.9420.8050.9750.9340.6170.9320.8680.8680.9740.9600.7720.7660.8790.9501.0000.007
UnitNumber-0.0090.0010.0010.003-0.0090.0000.0050.0000.0020.0180.014-0.006-0.0010.0010.0020.0030.0010.0020.0030.001-0.000-0.0010.0030.0030.0060.0071.000

Missing values

2024-06-01T20:45:03.031897image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-01T20:45:03.754625image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

UnitNumberCycleOperation Setting 1Operation Setting 2Operation Setting 3Sensor Measure 1Sensor Measure 2Sensor Measure 3Sensor Measure 4Sensor Measure 5Sensor Measure 6Sensor Measure 7Sensor Measure 8Sensor Measure 9Sensor Measure 10Sensor Measure 11Sensor Measure 12Sensor Measure 13Sensor Measure 14Sensor Measure 15Sensor Measure 16Sensor Measure 17Sensor Measure 18Sensor Measure 19Sensor Measure 20Sensor Measure 21RUL
01142.00490.8400100.0445.00549.681343.431112.933.915.70137.362211.868311.321.0141.69129.782387.998074.839.33350.023302212100.0010.626.3670321
11220.00200.7002100.0491.19606.071477.611237.509.3513.61332.102323.668713.601.0743.94312.592387.738046.139.19130.023612324100.0024.3714.6552320
21342.00380.8409100.0445.00548.951343.121117.053.915.69138.182211.928306.691.0141.66129.622387.978066.629.40070.023292212100.0010.486.4213319
31442.00000.8400100.0445.00548.701341.241118.033.915.70137.982211.888312.351.0241.68129.802388.028076.059.33690.023282212100.0010.546.4176318
41525.00630.620760.0462.54536.101255.231033.597.059.00174.821915.227994.940.9336.48164.112028.087865.8010.83660.02305191584.9314.038.6754317
51634.99960.8400100.0449.44554.771352.871117.015.487.97193.822222.778340.001.0241.44181.902387.878054.109.33460.023302223100.0014.918.9057316
6170.00190.0001100.0518.67641.831583.471393.8914.6221.58552.452387.929050.501.3046.94520.482387.898127.928.39600.033912388100.0038.9323.4578315
71841.99810.8400100.0445.00549.051344.161110.773.915.69137.132211.928307.281.0141.60129.652387.978075.999.36790.023292212100.0010.556.2787314
81942.00160.8400100.0445.00549.551342.851101.673.915.70138.022211.908307.811.0241.44129.652388.008071.139.33840.023282212100.0010.636.3055313
911025.00190.621760.0462.54536.351251.911041.377.059.01174.701915.238005.830.9436.24164.082028.137869.4110.91410.02305191584.9314.348.6119312
UnitNumberCycleOperation Setting 1Operation Setting 2Operation Setting 3Sensor Measure 1Sensor Measure 2Sensor Measure 3Sensor Measure 4Sensor Measure 5Sensor Measure 6Sensor Measure 7Sensor Measure 8Sensor Measure 9Sensor Measure 10Sensor Measure 11Sensor Measure 12Sensor Measure 13Sensor Measure 14Sensor Measure 15Sensor Measure 16Sensor Measure 17Sensor Measure 18Sensor Measure 19Sensor Measure 20Sensor Measure 21RUL
6123924924642.00490.8400100.0445.00549.661360.691143.753.915.69141.982212.408362.881.0442.40133.312388.548130.979.12490.023352212100.0010.746.425810
6124024924741.99970.8409100.0445.00549.791359.281139.923.915.69142.282212.458375.361.0442.61134.152388.598133.749.12190.023342212100.0010.716.44229
6124124924820.00160.7011100.0491.19607.491494.101265.209.3513.60341.742324.638780.121.0944.83322.012388.748106.609.01650.033682324100.0024.8914.90158
6124224924942.00460.8405100.0445.00549.341365.591138.333.915.69141.442212.528370.781.0542.56133.842388.598136.449.08220.023332212100.0010.886.45307
6124324925025.00000.620060.0462.54537.341269.561057.407.059.00179.151915.848049.770.9437.21167.632028.757909.6110.70290.02307191584.9314.558.68056
612442492519.99980.2500100.0489.05605.331516.361315.2810.5215.46404.592319.668840.161.2746.08380.162388.738185.698.45410.033722319100.0029.1117.52345
612452492520.00280.0015100.0518.67643.421598.921426.7714.6221.57567.592388.479117.121.3148.04535.022388.468185.478.22210.033962388100.0039.3823.71514
612462492530.00290.0000100.0518.67643.681607.721430.5614.6221.57569.042388.519126.531.3148.24535.412388.488193.948.25250.033952388100.0039.7823.82703
6124724925435.00460.8400100.0449.44555.771381.291148.185.487.96199.932223.788403.641.0542.53187.922388.838125.649.05150.023372223100.0015.269.07742
6124824925542.00300.8400100.0445.00549.851369.751147.453.915.69142.472212.528391.311.0542.60134.322388.668144.339.12070.023332212100.0010.666.43411